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Two fixes for Gemma4UnifiedForConditionalGeneration audio inputs: 1. Requests with multiple audios of different lengths killed the EngineCore. _call_hf_processor stores per-audio features unpadded so cache entries are batch-independent, but MultiModalFieldConfig.batched() does not re-pad ragged items at batch time - it yields a plain list, on which _process_audio_input called .squeeze(1) (AttributeError -> EngineDeadError). Re-pad to the batch max in _parse_and_validate_audio_input via _pad_ragged_audio_features, and drop the .squeeze(1) no-ops that corrupt single-frame audio. 2. The unified variant inherited the tower variant's mel+conv placeholder count formula, which undercounts the encoder-free frame count ceil(L / 640) by one whenever L % 640 is in [1, 160], silently dropping the trailing audio embedding (or failing placeholder matching with the processor cache disabled). Override get_audio_repl in Gemma4UnifiedProcessingInfo to match the HF processor's mask.sum(). Add processor-level regression tests for ragged batching, token count parity with the HF feature extractor, and multi-audio processing. Co-authored-by: Claude Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: Sidd065 <sidd.jml@gmail.com>
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This pull request has merge conflicts that must be resolved before it can be |
Signed-off-by: Siddharth Nachane <78248734+Sidd065@users.noreply.github.com>
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This pull request has merge conflicts that must be resolved before it can be |
Purpose
Fix two bugs that break audio inputs for
Gemma4UnifiedForConditionalGeneration(
google/gemma-4-12B-it):EngineCore crash on multi-audio requests with different clip lengths.
Gemma4MultiModalProcessor._call_hf_processordeliberately stores eachaudio's features unpadded (so processor-cache entries are independent of the
batch they were co-processed with), but
MultiModalFieldConfig.batched()does not re-pad ragged items at batch time — it yields a plain Python list,
on which
_process_audio_inputcalled.squeeze(1):AttributeError: 'list' object has no attribute 'squeeze'insideEngineCore.execute_model→EngineDeadError, killing the server. A singleaudio (or equal-length audios) takes the tensor fast path, which is why this
only bites with ≥2 clips of different durations — e.g. a long call recording
chunked into <30s pieces.
Wrong audio placeholder count for the unified (encoder-free) variant.
The unified model inherits the tower variant's mel+conv sequence-length
arithmetic (
_compute_audio_num_tokens), but its HF processor insertsexactly
ceil(samples / 640)audio tokens (one per 640-sample frame, nodownsampling —
Gemma4UnifiedProcessor.replace_audio_tokenusesinput_features_mask.sum()). The two formulas diverge by one token wheneversamples % 640 ∈ [1, 160](25% of arbitrary lengths): with the processorcache enabled the surplus trailing embedding is silently dropped
(quality degradation, no error); with
--mm-processor-cache-gb 0theplaceholder sequence match fails and the request errors out.
Changes:
gemma4_mm.py: new_pad_ragged_audio_features()applied in_parse_and_validate_audio_input— re-pads ragged lists to the batch max andrebuilds the mask at the single parse point shared by the tower and unified
models, covering both within-request and cross-request grouping
(
group_and_batch_mm_kwargsbatches audio items from different requests intoone forward). Corrected the stale comment claiming
batched()re-pads, andremoved the
.squeeze(1)no-ops (they corrupt thes == 1edge case, i.e.clips ≤ 640 samples).
gemma4_unified.py:Gemma4UnifiedProcessingInfo.get_audio_reploverridecomputing
ceil(audio_len / audio_samples_per_token), matching the HFprocessor exactly. Deliberately uncapped, mirroring HF (which neither
truncates features nor caps placeholders); capping without truncating would
recreate the mismatch for >30s clips.
Relationship to existing open PRs (#47459, #45887, #44546, #42573, #39459):
those address only bug 1, with the pad-and-stack normalization placed in
_process_audio_input. This PR is materially different: it additionally fixesbug 2 (the unified-variant token-count mismatch, not touched by any of the
above), normalizes at
_parse_and_validate_audio_inputso both Gemma4 modelclasses are covered by one code path, and adds unified-variant regression
tests. Happy to rebase/reduce the overlap if one of the sibling PRs lands
first.
Test Plan
Processor-level (CPU, no weights loaded):
New regression tests:
test_audio_field_batching_repads_ragged_lengths— ragged clips throughbatched()→ list →_pad_ragged_audio_features→ padded tensors + mask(bug 1)
test_audio_repl_token_count_matches_hf_processor— vLLM placeholder countvs HF
input_features_mask.sum()atL = 640k + r,r ∈ {0, 1, 160, 161, 639}(fails before this PR forr ∈ {1, 160}) (bug 2)test_multi_audio_apply_different_lengths— two clips of different lengthsthrough
processor.apply; per-item feature shapes and placeholder lengthsE2E (1× RTX PRO 6000 Blackwell):
vllm serve google/gemma-4-12B-it --limit-mm-per-prompt '{"image": 0, "audio": 20}', then chat completions withmultiple
input_audioparts of different durations; single clips withsamples % 640 ∈ [1, 160]; repeated with--mm-processor-cache-gb 0.Test Result
Before:
EngineDeadError—the server process dies.
% 640 = 80) with--mm-processor-cache-gb 0:RuntimeError: Expected there to be 1 prompt placeholders corresponding to 1 audio itemsat processing time; with thecache enabled it "succeeds" while silently dropping the trailing embedding.
AttributeErroron list / off-by-one token count).After:
test_gemma4_unified.pyandtest_gemma4.pysuites pass (26 pre-existingtower tests green — no regression).
≤20 ×25 s clips per request, 581 audio items total) × multiple passes
including a concurrency sweep up to 128 in-flight requests — 0 errors,
0 engine deaths.
AI assistance was used for this change (Claude)